Anatomy of a Smart Contract

By: Steve Jones

14, February, 2017

Categories:

Blockchain -

Introduction

Although the concept of smart contracts was introduced in the late 1990s by Nick Szabo, it remained an abstract concept until the Frontier release of Ethereum in 2015. That release was the first implementation of smart contracts, and was enough to move them from a concept to a reality. However, as the community experimented with the technology, it became clear that new requirements would need to be met before they could be deployed at scale.

This paper places the development of smart contracts in a historical context, and explains a method for addressing new requirements that maintains the benefits of the initial implementation in Ethereum. With these modifications, smart contracts still deliver on their core promise, but can be developed in a flexible environment that meets the needs of the enterprise.

A Brief History of Architectures

It’s easiest to understand the evolution of smart contracts by seeing their development in the context of how software development paradigms have shifted over time.

In the 1990s, the PC revolution led enterprises to move to client/server applications, and away from mainframe-based, dumb terminal or single-tiered applications. Enterprise developers started building 2-tiered applications where data was separate from the client or business logic.

Initially, client applications held the business logic and UI code. Tools like PowerBuilder and Visual Basic raced to have the best UI frameworks, control libraries and developer experiences so enterprise developers could create the best-looking, most performant experiences. As developers added more business logic, control presentation, input and application interoperability functionality, the client installation footprint on PC hard disks grew tremendously. That bloat led to them being called “fat clients” because of their deployment requirement to fully install all the code and libraries.

Minimizing the use of network bandwidth between the data and logic tiers was an important design objective. Clients that requested too much data could timeout, crash the server, or clog the network. Optimizing database performance became a critical part of application design. Stored Procedures helped improve performance by allowing data access and validation logic to run on the data tier. They also simplified development by exposing logic for clients to perform create, read, update and delete (CRUD) operations without hand-writing SQL statements.

Over time, problems emerged. Dependencies on stored procedures and lifecycle management challenges reduced agility and increased vendor lock-in. The advent of the internet pressured developers to create applications that could handle thousands of clients concurrently rather a few hundred. And while technology solutions like Database Connection Pooling, RPC-based remote object invocation and CORBA, DCOM, and Java RMS mitigated the problems, they didn’t address the fundamental issues.

After several years of trial and error, a new paradigm was born: a 3-tiered architecture, with separate presentation, business logic and data tiers. The data tier continued to be RDBMS Servers, while database connection pooling, distributed transaction coordination, and remote object techniques were run in the business logic tier. The presentation tier became web servers with HTML and JavaScript.

The tiers represented actual hardware components, servers and networking devices that supported software layers defined by the type of logic that ran in them. The presentation layer contained user interface logic such as input validation, control focus to guide users through application input flow, data presentation and data binding. The business logic layer defined the API for the presentation, or any other system that wanted to interact with the application. Eventually, this layer became stateless, which allowed it to scale horizontally using network load balancers. It also allowed for the complete abstraction of the data layer, and provided composite interfaces across multiple back ends for better integration.

The data logic layer contained logic for creating, reading, updating and deleting data for the underlying data platform. That was primarily written in SQL-based stored procedures, but newer big data, No-SQL and blockchain were also included.

With this architecture, deployment was much simpler; a developer could simply map the logic layer on top of the physical tiers based on the scale, security and performance that was needed. This separation of concerns let layers scale horizontally, by adding more computers in the tier, or vertically, by upgrading the amount of resources for single computers. This approach removed brittle, tight couplings between systems, making modularity, versioning, (.dll hell) and client deployment much easier. Not surprisingly, Separation of Concerns became a best practice. It continues to be an important paradigm today in modern micro-service architectures.

The Initial Smart Contract Implementation

Seeing the initial development of smart contracts in a historical context highlights the limitations of the initial implementation, and provides guidance on how smart contracts will need to evolve. [i]

The initial release of smart contracts in Ethereum was designed to give parties that don’t trust each other a way to enter into an agreement, where they can be confident that the transaction will unfold as they intend, and where they can verify the status of the contract or transaction at any time.

To achieve those design goals, the initial smart contracts implementation didn’t follow the typical patterns for application development. Specifically, it included the logic, properties, and data in a single package, essentially collapsing the business and data logic layers into a single layer, which are then written to the blockchain. That provides the immutability, deterministic execution and transparency required in untrusted environments.

Contracts are generally written in Solidity (Serpent and others are supported as well) where data structures, functions for business logic, and authorization based on addresses are checked. The source code is compiled into bytecode, and deployed to all nodes on the blockchain for execution. When a DApp is configured properly, it sends a message or transaction to a function of the corresponding smart contract. To do that, it needs the ABI (Application Binary Interface) to correctly format the message and digitally sign it for submission. Once the message is received by a node on the network, it is replicated to all the nodes on the network for execution.

Unfortunately, the initial approach presents challenges that are often difficult for DApp (Distributed Application) developers. A DApp’s presentation logic has dependencies at runtime, such as an address of a node on the network (DNS, IP, URI), as well as a port to communicate with [ii]. The DApp also needs to know the Ethereum address of the smart contract that is deployed on the blockchain, which is not easily discoverable. Finally, it also needs access to secure private keys, which can be manually inserted by using a file, a blockchain wallet, or a secure device.

The current smart contract model is optimized for public, untrusted networks. Replication helps provide authenticity and agreement across untrusted networks, but it comes at a cost; if there are 1,000 nodes on the network, the smart contract function of a single DApp is executed 1,000 times each time it’s requested. The slowest node in the blockchain defines the upper limit of execution speed for the entire network, and the more logic included in the smart contracts, the slower the network executes. The performance implications can be crippling for business-to-business scenarios in trusted or semi-trusted environments.

Providing Flexibility with Cryptlets

There should be a choice for software architects that are designing applications that use smart contracts. While scenarios in untrusted or public environments may require a unified smart contract package, applications running in trusted or semi-trusted networks would benefit from a typical enterprise application development pattern.

If we think about blockchains as the data layer deployed in a three-tiered architecture, only a subset of the features of a smart contract should be implemented on the blockchain[iii]. Complex business logic should be removed from the execution path, which allows the data tier to be optimized to reflect the distributed nature of the network. [iv]

To pull business logic up above the blockchain to a separate middle layer, the logic code needs access to a variety of services, including secure execution, attestation, identity, cryptographic support, data formatting, reliable messaging, triggers, and the ability to bind that code to schema in specific smart contracts on any number of blockchains. Those services can be provided in a fabric, where the individual pieces of code that support the smart contracts can execute, send transactions to blockchain nodes, and be bound to the schema in the data tier.

We refer to these code blocks as Cryptlets, and the execution environment they run is called the Cryptlet Fabric.

Refactoring a Smart Contract

To get a clearer picture of how this separation of concerns is achieved, we can separate out the different portions of a smart contract into discrete components. These basic components are the properties (static and variable), the logic and the ledger. Each of these components can be mapped directly into technical concepts. Properties represent a data schema, logic represents code, and the ledger corresponds to a database. Once each of these components are defined, they can be deployed to environments that are optimized for their function.

Recall that in today’s version of smart contracts, there is a single deployment model to a single computer or a node. That node is replicated so that each node runs every smart contract, and produces the exact results for each contract step that all other nodes produce. The contract is deterministic, which means that each copy of the contract produces the same output when the inputs are identical.

Figure 1: Creating a Smart Contract

With Cryptlets, once we separate the data and ledger from the logic, we can create a platform for the logic to run optimally. The contract’s logic is packaged into a Cryptlet, which is a block or blocks of code that run inside a container, inside a fabric.

Figure 2: Smart Contracts Reimagined

The smart contract is now a composite of the on-chain Solidity smart contract that defines the data schema on the blockchain, and a Cryptlet that hosts the logic for the smart contract. These Cryptlets can be run on a different computer or the cloud, rather than the actual nodes, and as a result, do not need to be executed by every node on the network.

Cryptlets execute in a secure computational environment, and have the cryptographic primitives that allow them to work directly with blockchains, thereby extending smart contracts off the blockchain within the same security envelope.

A New Trust Model

With the introduction of a new computational tier, a new trust model can be applied to blockchain applications. Public blockchains like Bitcoin and Ethereum operate in a trustless model, where trust is placed in the cryptography (hashes and digital signatures) and consensus algorithms such as proof-of-work that mitigate the threat from malicious nodes. Counterparties in transactions are pseudo-anonymous, and if things go wrong, the parties have little or no recourse.

Consortium blockchains are different. They can deploy applications to permissioned blockchains using a variety of blockchain cores, such as Hyperledger Fabric or Monax. These generally aren’t trustless environments since participants’ addresses are mapped to known identities in an identity service like Okta or Azure Active Directory. These can be called semi-trusted networks, where the parties know who they are transacting with, and if someone misbehaves, existing models and legal frameworks provide an avenue for corrections or recourse. As a result, the network may use other consensus algorithms like proof-of-stake or PBFT, and achieve a much higher transaction rate.

Cryptlets introduce a different implicit trust model, where they themselves execute with a known identity in a secure environment, and produce cryptographic proofs for all their outputs. If they are given permissions by another party, they can also assume that party’s identity, and digitally sign transactions on its behalf. This trust model can be used with either a trustless (public) network or semi-trusted network (consortium), essentially creating a third area that can straddle both network types.

Advantages and Implications

There are many advantages that come with this new framework. Possibly the most important aspect of the Cryptlet approach is the choice and flexibility it provides. Developers can work in the languages they prefer, and deploy each part of the smart contract to an environment that meets their needs for scale, lifecycle management, monitoring, transparency, and immutability. It also means that:

Data schema and validation logic on the blockchain is maintained

There is still distributed data and trust at the data layer

The logic layer has discretionary trust between concerned parties only

Since the logic isn’t dependent on the blockchain itself, trusted data and logic is portable across blockchains

Confidentiality is easier to achieve by putting code, or counter party secrets like keys and independent terms, within a Cryptlet

It’s possible to version logic independently from data and schema

Moving Forward with Smart Contracts

In summary, smart contracts can be decomposed into layers and deployed into separate tiers providing the goodness of a blockchain ledger for the database and Cryptlets for scalable smart contracts. The implementation is a choice by the application architect:

A Solidity-based (or other language) smart contract that is 100% on the blockchain, or combined with a side-chain for privacy. This preserves the atomicity of the original smart contract design, which may be optimal for untrusted or public blockchain applications.

Database schema and validation logic on the blockchain, with the smart contract logic and behavior running in Cryptlets. This keeps blockchain code to a minimum, and provides the benefits you’d expect for typical cloud services.

A UTXO Transaction Database and a Cryptlet hosting the smart contract data schema, logic and behavior. This provides similar benefits to the second case, but applies to UTXO blockchain scenarios.

And now, a quick teaser for the next white paper: Bletchley: A Cryptlet Fabric in Depth. In that paper, we’ll explore how the Fabric makes a 3-tiered blockchain architecture real. The following diagram will play a big part, but we’ll let you have a peek:

Figure 5: The Cryptlet Fabric

by Marley Gray & Craig Hajduk – Microsoft

Footnotes

[i] It’s important to realize that smart contracts are not native to all blockchains. UTXO blockchain nodes usually have very simple virtual machines. These systems generally don’t support complex data structures; assets on a UTXO blockchains have data properties but no defined schema or behavior. They don’t allow behaviors associated with smart contracts, but use accounts, UTXOs and virtual machine OPCODES to connect applications. These systems are generally better for scenarios like tracking asset lineage, such as a digital bearer scenario. Bitcoin’s entire design is optimized for this use case.

[ii] One interesting nuance here is that the presentation layer of DApps is often centralized on a web server, which goes against the idea of a truly decentralized application. Building a truly decentralized, un-censorable application would require a different approach.

[iii] The parts of the traditional smart contract package that are appropriate for being on the blockchain would be database schema, validation and verification of transactions that append to the ledger, and query optimization logic for reading the ledger.

[iv] For this paper, we’ll focus on implementing separation of concerns at the business and data layers. We’ll address the presentation or user interface later in a future paper.

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